Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River

Recent approaches toward solving the regression problems which are characterized by dynamic and nonlinear pattern such as machine learning modeling (including artificial intelligence (AI) approaches) have proven to be useful and successful tools for prediction. Approaches that integrate predictive model with optimization algorithm such as hybrid soft computing have resulted in the enhancement of the accuracy and preciseness of models during problem predictions. In this research, the implementation of hybrid evolutionary model based on integrated support vector regression (SVR) with firefly algorithm (FFA) was investigated for water quality indicator prediction. The monthly water quality indicator (WQI) that was used to test the hybrid model over a period of 10 years belongs to the Euphrates River, Iraq. The use of the WQI as an application for this research was stimulated based on the fact that WQI is usually calculated using a manual formulation which takes much time, efforts and occasionally may be associated with errors that were not intended during the subindex calculations. The parameters considered during the formulation of the prediction model were water quality parameters as input and WQI as output. The SVR model was used to verify the accuracy of the inspected SVR–FFA model. Different statistical metrics such as best fit of goodness and absolute error measures were used to evaluate the model. The performance of the hybrid model in recognizing the dynamic and nonlinear pattern characteristics was high and remarkable compared to the pure model. The SVR–FFA model was also demonstrated to be a good and robust soft computing technique toward the prediction of WQI. The proposed model enhanced the absolute error measurements (e.g., root mean square error and mean absolute error) over the SVR-based model by 42 and 58%, respectively

Quantification of Morphometric Analysis using Remote Sensing and GIS Techniques in the Qa’ Jahran Basin, Thamar Province, Yemen

Demand for irrigation water increases day by day along with meteorological vagaries and extension of irrigated area in the drought-prone Jahran district. The study is aimed at studying the morphometric parameters of the Qa#39 Jahran basin in Jahran district and on their relevance in water resource management. The drainage network was prepared from the ASTER (DEM) and verified with survey and mineral resources board of Yemenrsquos maps in GIS environment.Quantitative morphometric analysis was carried out for different sub basins for linear aspects, areal aspects and relief aspects.The results of the morphometric analysis reveal that the QarsquoJahran basin is of dendritic pattern, high erosion activity, basin is underlined by uniform materials, basin is flat, Drainage density is moderate spacing streams, permeable sub soil materials with dense vegetated cover and low relief (alluvial plain), susceptible to flooding, gully erosion, enhanced ground water recharge potentiality, Form factor and circularity ratio results represent an elongated shape, have a flatter peak flow for longer duration and drainage system were subject to less structurally controlled on the drainage development in over all the basin, low relief for most portion of the basin, high surface runoff and high susceptibility of the basin for both soil erosion and flooding.The higher slope gradient in the study area is contributed by the eruption of basaltic flow in northern, eastern and western parts. Higher slope gradient results in rapid runoff with potential soil loss or erosion. The Qa#39 Jahran basin relief value is 110m for sub basin 1 to 640m for sub basin 2 indicates low infiltration and high runoff conditions. The ruggedness number ofthe subbasins 1 and 2 indicates higher soil erosion susceptibility